A probabilistic approach to structural change prediction in evolving social networks
Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.
Journal: Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Pages: 996-1001
ISBN: 9780769547992
DOI: 10.1109/ASONAM.2012.173
Abstract:We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server. © 2012 IEEE.
https://eprints.bournemouth.ac.uk/20437/
Source: Scopus
A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.
Journal: 2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM)
Pages: 996-1001
ISBN: 978-1-4673-2497-7
DOI: 10.1109/ASONAM.2012.173
https://eprints.bournemouth.ac.uk/20437/
Source: Web of Science (Lite)
A probabilistic approach to structural change prediction in evolving social networks
Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.
Pages: 996-1001
DOI: 10.1109/ASONAM.2012.173
https://eprints.bournemouth.ac.uk/20437/
Source: Manual
Preferred by: Marcin Budka
A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks.
Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K. and Budka, M.
Journal: ASONAM
Pages: 996-1001
Publisher: IEEE Computer Society
ISBN: 978-0-7695-4799-2
DOI: 10.1109/ASONAM.2012.173
https://eprints.bournemouth.ac.uk/20437/
https://ieeexplore.ieee.org/xpl/conhome/6423126/proceeding
Source: DBLP
Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
Authors: Juszczyszyn, K., Gonczarek, A., Tomczak, J., Musial, K. and Budka, M.
Conference: International Workshop on Complex Social Network Analysis (CSNA 2012) co-located with International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Abstract:We propose a predictive model of structural changes in elementary subgraphs of social network based on Mixture of Markov Chains. The model is trained and verified on a dataset from a large corporate social network analyzed in short, one day-long time windows, and reveals distinctive patterns of evolution of connections on the level of local network topology. We argue that the network investigated in such short timescales is highly dynamic and therefore immune to classic methods of link prediction and structural analysis, and show that in the case of complex networks, the dynamic subgraph mining may lead to better prediction accuracy. The experiments were carried out on the logs from the Wroclaw University of Technology mail server.
https://eprints.bournemouth.ac.uk/20437/
Source: BURO EPrints